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@@ -0,0 +1 @@ +/fastrl-0.0.47.tar.gz diff --git a/python-fastrl.spec b/python-fastrl.spec new file mode 100644 index 0000000..72e6b13 --- /dev/null +++ b/python-fastrl.spec @@ -0,0 +1,422 @@ +%global _empty_manifest_terminate_build 0 +Name: python-fastrl +Version: 0.0.47 +Release: 1 +Summary: fastrl is a reinforcement learning library that extends Fastai. This project is not affiliated with fastai or Jeremy Howard. +License: Apache Software License 2.0 +URL: https://github.com/josiahls/fastrl/tree/main/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/10/2e/0984dba8ba770cf443b16ee9ffec1b07daf26e578d43bb3a9c0b73b7d943/fastrl-0.0.47.tar.gz +BuildArch: noarch + +Requires: python3-pip +Requires: python3-packaging +Requires: python3-torch +Requires: python3-torchdata +Requires: python3-gym +Requires: python3-pyopengl +Requires: python3-pyglet +Requires: python3-tensorboard +Requires: python3-pygame +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-sklearn +Requires: python3-fastcore +Requires: python3-fastprogress +Requires: python3-nbformat +Requires: python3-gym[all] +Requires: python3-jupyterlab +Requires: python3-nbdev +Requires: python3-pre-commit +Requires: python3-ipywidgets +Requires: python3-moviepy +Requires: python3-pygifsicle +Requires: python3-aquirdturtle-collapsible-headings +Requires: python3-plotly +Requires: python3-matplotlib-inline +Requires: python3-wheel +Requires: python3-twine +Requires: python3-fastdownload +Requires: python3-watchdog[watchmedo] +Requires: python3-graphviz +Requires: python3-typing-extensions +Requires: python3-spacy + +%description +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +[](https://github.com/josiahls/fastrl/actions?query=workflow%3A%22Fastrl+Testing%22) +[](https://pypi.python.org/pypi/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl-dev) +[](https://pypi.python.org/pypi/fastrl) +[](https://pypi.python.org/pypi/fastrl) +> Warning: This is in alpha, and so uses latest torch and torchdata, +> very importantly torchdata. The base API, while at the point of +> semi-stability, might be changed in future versions, and so there will +> be no promises of backward compatiblity. For the time being, it is +> best to hard-pin versions of the library. +> Warning: Even before fastrl==2.0.0, all Models should converge +> reasonably fast, however HRL models `DADS` and `DIAYN` will need +> re-balancing and some extra features that the respective authors used. +# Overview +Fastai for computer vision and tabular learning has been amazing. One +would wish that this would be the same for RL. The purpose of this repo +is to have a framework that is as easy as possible to start, but also +designed for testing new agents. +This version fo fastrl is basically a wrapper around +[torchdata](https://github.com/pytorch/data). +It is built around 4 pipeline concepts (half is from fastai): +- DataLoading/DataBlock pipelines +- Agent pipelines +- Learner pipelines +- Logger plugins +Documentation is being served at https://josiahls.github.io/fastrl/ from +documentation directly generated via `nbdev` in this repo. +Basic DQN example: +``` python +from fastrl.loggers.core import * +from fastrl.loggers.vscode_visualizers import * +from fastrl.agents.dqn.basic import * +from fastrl.agents.dqn.target import * +from fastrl.data.block import * +from fastrl.envs.gym import * +import torch +``` +``` python +# Setup Loggers +logger_base = ProgressBarLogger(epoch_on_pipe=EpocherCollector, + batch_on_pipe=BatchCollector) +# Setup up the core NN +torch.manual_seed(0) +model = DQN(4,2) +# Setup the Agent +agent = DQNAgent(model,[logger_base],max_steps=10000) +# Setup the DataBlock +block = DataBlock( + GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True), # We basically merge 2 steps into 1 and skip. + (GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True,n=100,include_images=True),VSCodeTransformBlock()) +) +dls = L(block.dataloaders(['CartPole-v1']*1)) +# Setup the Learner +learner = DQNLearner(model,dls,logger_bases=[logger_base],bs=128,max_sz=20_000,nsteps=2,lr=0.001, + batches=1000, + dp_augmentation_fns=[ + # Plugin TargetDQN code + TargetModelUpdater.insert_dp(), + TargetModelQCalc.replace_dp() + ]) +learner.fit(10) +#learner.validate() +``` +# Whats new? +As we have learned how to support as many RL agents as possible, we +found that `fastrl==1.*` was vastly limited in the models that it can +support. `fastrl==2.*` will leverage the `nbdev` library for better +documentation and more relevant testing, and `torchdata` is the base +lib. We also will be building on the work of the `ptan`<sup>1</sup> +library as a close reference for pytorch based reinforcement learning +APIs. +<sup>1</sup> “Shmuma/Ptan”. Github, 2020, +https://github.com/Shmuma/ptan. Accessed 13 June 2020. +## Install +## PyPI +Below will install the alpha build of fastrl. +**Cuda Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu113` +**Cpu Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cpu` +## Docker (highly recommend) +Install: +[Nvidia-Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) +Install: [docker-compose](https://docs.docker.com/compose/install/) +``` bash +docker-compose pull && docker-compose up +``` +## Contributing +After you clone this repository, please run `nbdev_install_hooks` in +your terminal. This sets up git hooks, which clean up the notebooks to +remove the extraneous stuff stored in the notebooks (e.g. which cells +you ran) which causes unnecessary merge conflicts. +Before submitting a PR, check that the local library and notebooks +match. The script `nbdev_clean` can let you know if there is a +difference between the local library and the notebooks. \* If you made a +change to the notebooks in one of the exported cells, you can export it +to the library with `nbdev_build_lib` or `make fastai2`. \* If you made +a change to the library, you can export it back to the notebooks with +`nbdev_update_lib`. + +%package -n python3-fastrl +Summary: fastrl is a reinforcement learning library that extends Fastai. This project is not affiliated with fastai or Jeremy Howard. +Provides: python-fastrl +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-fastrl +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +[](https://github.com/josiahls/fastrl/actions?query=workflow%3A%22Fastrl+Testing%22) +[](https://pypi.python.org/pypi/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl-dev) +[](https://pypi.python.org/pypi/fastrl) +[](https://pypi.python.org/pypi/fastrl) +> Warning: This is in alpha, and so uses latest torch and torchdata, +> very importantly torchdata. The base API, while at the point of +> semi-stability, might be changed in future versions, and so there will +> be no promises of backward compatiblity. For the time being, it is +> best to hard-pin versions of the library. +> Warning: Even before fastrl==2.0.0, all Models should converge +> reasonably fast, however HRL models `DADS` and `DIAYN` will need +> re-balancing and some extra features that the respective authors used. +# Overview +Fastai for computer vision and tabular learning has been amazing. One +would wish that this would be the same for RL. The purpose of this repo +is to have a framework that is as easy as possible to start, but also +designed for testing new agents. +This version fo fastrl is basically a wrapper around +[torchdata](https://github.com/pytorch/data). +It is built around 4 pipeline concepts (half is from fastai): +- DataLoading/DataBlock pipelines +- Agent pipelines +- Learner pipelines +- Logger plugins +Documentation is being served at https://josiahls.github.io/fastrl/ from +documentation directly generated via `nbdev` in this repo. +Basic DQN example: +``` python +from fastrl.loggers.core import * +from fastrl.loggers.vscode_visualizers import * +from fastrl.agents.dqn.basic import * +from fastrl.agents.dqn.target import * +from fastrl.data.block import * +from fastrl.envs.gym import * +import torch +``` +``` python +# Setup Loggers +logger_base = ProgressBarLogger(epoch_on_pipe=EpocherCollector, + batch_on_pipe=BatchCollector) +# Setup up the core NN +torch.manual_seed(0) +model = DQN(4,2) +# Setup the Agent +agent = DQNAgent(model,[logger_base],max_steps=10000) +# Setup the DataBlock +block = DataBlock( + GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True), # We basically merge 2 steps into 1 and skip. + (GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True,n=100,include_images=True),VSCodeTransformBlock()) +) +dls = L(block.dataloaders(['CartPole-v1']*1)) +# Setup the Learner +learner = DQNLearner(model,dls,logger_bases=[logger_base],bs=128,max_sz=20_000,nsteps=2,lr=0.001, + batches=1000, + dp_augmentation_fns=[ + # Plugin TargetDQN code + TargetModelUpdater.insert_dp(), + TargetModelQCalc.replace_dp() + ]) +learner.fit(10) +#learner.validate() +``` +# Whats new? +As we have learned how to support as many RL agents as possible, we +found that `fastrl==1.*` was vastly limited in the models that it can +support. `fastrl==2.*` will leverage the `nbdev` library for better +documentation and more relevant testing, and `torchdata` is the base +lib. We also will be building on the work of the `ptan`<sup>1</sup> +library as a close reference for pytorch based reinforcement learning +APIs. +<sup>1</sup> “Shmuma/Ptan”. Github, 2020, +https://github.com/Shmuma/ptan. Accessed 13 June 2020. +## Install +## PyPI +Below will install the alpha build of fastrl. +**Cuda Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu113` +**Cpu Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cpu` +## Docker (highly recommend) +Install: +[Nvidia-Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) +Install: [docker-compose](https://docs.docker.com/compose/install/) +``` bash +docker-compose pull && docker-compose up +``` +## Contributing +After you clone this repository, please run `nbdev_install_hooks` in +your terminal. This sets up git hooks, which clean up the notebooks to +remove the extraneous stuff stored in the notebooks (e.g. which cells +you ran) which causes unnecessary merge conflicts. +Before submitting a PR, check that the local library and notebooks +match. The script `nbdev_clean` can let you know if there is a +difference between the local library and the notebooks. \* If you made a +change to the notebooks in one of the exported cells, you can export it +to the library with `nbdev_build_lib` or `make fastai2`. \* If you made +a change to the library, you can export it back to the notebooks with +`nbdev_update_lib`. + +%package help +Summary: Development documents and examples for fastrl +Provides: python3-fastrl-doc +%description help +<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! --> +[](https://github.com/josiahls/fastrl/actions?query=workflow%3A%22Fastrl+Testing%22) +[](https://pypi.python.org/pypi/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl) +[](https://hub.docker.com/repository/docker/josiahls/fastrl-dev) +[](https://pypi.python.org/pypi/fastrl) +[](https://pypi.python.org/pypi/fastrl) +> Warning: This is in alpha, and so uses latest torch and torchdata, +> very importantly torchdata. The base API, while at the point of +> semi-stability, might be changed in future versions, and so there will +> be no promises of backward compatiblity. For the time being, it is +> best to hard-pin versions of the library. +> Warning: Even before fastrl==2.0.0, all Models should converge +> reasonably fast, however HRL models `DADS` and `DIAYN` will need +> re-balancing and some extra features that the respective authors used. +# Overview +Fastai for computer vision and tabular learning has been amazing. One +would wish that this would be the same for RL. The purpose of this repo +is to have a framework that is as easy as possible to start, but also +designed for testing new agents. +This version fo fastrl is basically a wrapper around +[torchdata](https://github.com/pytorch/data). +It is built around 4 pipeline concepts (half is from fastai): +- DataLoading/DataBlock pipelines +- Agent pipelines +- Learner pipelines +- Logger plugins +Documentation is being served at https://josiahls.github.io/fastrl/ from +documentation directly generated via `nbdev` in this repo. +Basic DQN example: +``` python +from fastrl.loggers.core import * +from fastrl.loggers.vscode_visualizers import * +from fastrl.agents.dqn.basic import * +from fastrl.agents.dqn.target import * +from fastrl.data.block import * +from fastrl.envs.gym import * +import torch +``` +``` python +# Setup Loggers +logger_base = ProgressBarLogger(epoch_on_pipe=EpocherCollector, + batch_on_pipe=BatchCollector) +# Setup up the core NN +torch.manual_seed(0) +model = DQN(4,2) +# Setup the Agent +agent = DQNAgent(model,[logger_base],max_steps=10000) +# Setup the DataBlock +block = DataBlock( + GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True), # We basically merge 2 steps into 1 and skip. + (GymTransformBlock(agent=agent,nsteps=2,nskips=2,firstlast=True,n=100,include_images=True),VSCodeTransformBlock()) +) +dls = L(block.dataloaders(['CartPole-v1']*1)) +# Setup the Learner +learner = DQNLearner(model,dls,logger_bases=[logger_base],bs=128,max_sz=20_000,nsteps=2,lr=0.001, + batches=1000, + dp_augmentation_fns=[ + # Plugin TargetDQN code + TargetModelUpdater.insert_dp(), + TargetModelQCalc.replace_dp() + ]) +learner.fit(10) +#learner.validate() +``` +# Whats new? +As we have learned how to support as many RL agents as possible, we +found that `fastrl==1.*` was vastly limited in the models that it can +support. `fastrl==2.*` will leverage the `nbdev` library for better +documentation and more relevant testing, and `torchdata` is the base +lib. We also will be building on the work of the `ptan`<sup>1</sup> +library as a close reference for pytorch based reinforcement learning +APIs. +<sup>1</sup> “Shmuma/Ptan”. Github, 2020, +https://github.com/Shmuma/ptan. Accessed 13 June 2020. +## Install +## PyPI +Below will install the alpha build of fastrl. +**Cuda Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu113` +**Cpu Install** +`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cpu` +## Docker (highly recommend) +Install: +[Nvidia-Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) +Install: [docker-compose](https://docs.docker.com/compose/install/) +``` bash +docker-compose pull && docker-compose up +``` +## Contributing +After you clone this repository, please run `nbdev_install_hooks` in +your terminal. This sets up git hooks, which clean up the notebooks to +remove the extraneous stuff stored in the notebooks (e.g. which cells +you ran) which causes unnecessary merge conflicts. +Before submitting a PR, check that the local library and notebooks +match. The script `nbdev_clean` can let you know if there is a +difference between the local library and the notebooks. \* If you made a +change to the notebooks in one of the exported cells, you can export it +to the library with `nbdev_build_lib` or `make fastai2`. \* If you made +a change to the library, you can export it back to the notebooks with +`nbdev_update_lib`. + +%prep +%autosetup -n fastrl-0.0.47 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-fastrl -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.47-1 +- Package Spec generated @@ -0,0 +1 @@ +eab196c0cecbb15260026cbcd61a7664 fastrl-0.0.47.tar.gz |